4.3 Fuel economy

Efficient driving generally furnishes increment in fuel economy. And an increase in fuel economy shall provide reduction in energy consumption, tailpipe emissions and air pollution [2].

More efficient driving in CAEVs can be achieved through a variety of mechanisms, including optimal driving cycle, dynamic eco-routing, traffic flow smoothing, and speed harmonization.

A wide-scale deployment of CAEVs could facilitate vehicle platooning that could lead to improved aerodynamics. These advances in CAEVs could lead to considerable improvements in fuel economy.

### 4.4 Eco-driving and platooning

Eco-driving may include route planning, trajectory optimization, and driving behavior improvement. And it is an effective way to reduce vehicle fuel consumption and achieve significant reduction in carbon emissions.

Platooning is based on Cooperative ACC (CACC) technologies that use V2V communication to enable constant time-gap following and ad hoc joining and leaving the platoon. Platooning dynamically chains CAEVs to maximize fuel efficiency.

Platooning is appealing due to the fact that it provides energy savings from aerodynamic drafting, more stable vehicle following dynamics, reduced traffic flow disturbances as well as potential safety improvements.

national-level fuel use impacts. Table 1 shows the notations used in the equations

Total U.S. LDV fuel consumption for various CAV scenarios compared with the base conventional

i,j, q<sup>t</sup>

consumption per mile over and above the fuel consumption per mile including all

� 1, q i,j

� 1, s i,j

Using the notations in Table 1, the baseline conventional fuel use in the U.S.

<sup>∑</sup><sup>i</sup>∈I,j∈<sup>J</sup> VMTi,j

t Set of technologies T, {partial automation technology, full automation technology}

i,j Fuel impact estimated by partial automation technology t, on road type i, and time of day j

i,j VMT impact estimated by partial automation technology t, on road type i, and time of day j

i,j Fuel impact estimated by full automation technology t, on road type i, and time of day j

i,j VMT impact estimated by full automation technology t, on road type i, and time of day j

i,j Original fuel consumption rate (gallon per mile), on road type i, and time of day j

i,j Original vehicle miles traveled, on road type i, and time of day j

Notations used in equations for the computation of National fuel consumption impacts [16].

<sup>t</sup> <sup>¼</sup> FCi,j t FCi,j t�1

<sup>t</sup> <sup>¼</sup> VMTi,j

<sup>0</sup> <sup>∗</sup>FCi,j 0

t VMTi,j t�1

� � (3)

!

!

i,j are the fractional changes in the fuel

� 1 (1)

� 1 (2)

for the computation of national fuel consumption impacts [16].

Connected Autonomous Electric Vehicles as Enablers for Low-Carbon Future

DOI: http://dx.doi.org/10.5772/intechopen.84287

!

i,j:

t VMTi,j t�1

!

<sup>t</sup> <sup>¼</sup> FCi,j t FCi,j t�1

i,j, and s<sup>t</sup>

<sup>t</sup> <sup>¼</sup> VMTi,j

Referring to Table 1, the impacts r<sup>t</sup>

r i,j

p i,j

(without CAVs) is calculated as: [16]

i Set of road type I, {city, highways}

rt

Figure 3.

scenario [16].

qt

pt

st

Table 1.

19

FC<sup>0</sup>

VMT<sup>0</sup>

j Set of time of day J, {peak hours, non-peak hours}

impacts considered earlier, that is, [16]

and analogously for p<sup>t</sup>
